Tracking visual objects, such as representations of human faces, for online video applications and for the emerging field of computer vision remains challenging. Despite much research, many challenges remain for developing practical systems, such how to deal with changes in onscreen facial appearance, how to maintain tracking during sudden motion, and how to minimize jitter.
Conventionally, some face tracking techniques use particle based filter tracking, which can significantly improve performance. However, in real applications, this type of face tracking eventually fails due to complex background noise, appearance change, and fast motion. A more robust face tracking technique than can be provided by particle filter tracking alone is essential for practical systems, such as console games, instant messaging applications, movie editing, digital media production, etc.
In order to be practical, a face tracking system, e.g., for online video, should be fast, with a high detection rate; and efficient, with a low tracking failure rate. Such a face tracking system should track in real time yet not occupy a major share of computer processing resources, or be subject to significant jitter. Other processing tasks should be able to execute while face tracking is occurring.